Automated Assessment of White Matter Integrity in TBI Using Machine Learning

使用机器学习自动评估 TBI 中白质完整性

• 批准号: 8732156
• 负责人: Brian Allen Taylor
• 金额: --
• 依托单位: MICHAEL E DEBAKEY VA MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-07-01 至 2016-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8732156
• 关键词: Accounting; Address; Affect; Afghanistan; Algorithms; Alzheimer' s Disease; Anisotropy; Behavioral; Biological Markers; Blast Cell; Blast Injuries; Brain; Brain Injuries; Brain region; Chronic; Classification; Cognitive; Computer software; Conflict (Psychology); Data; Detection; Development; Diagnosis; Diffuse; Diffusion Magnetic Resonance Imaging; Disease; Extracellular Space; Fiber; Freedom; Goals; Head and neck structure; Image; Individual; Injury; Intervention; Iraq; Joints; Life; Link; Machine Learning; Magnetic Resonance Imaging; Measures; Military Personnel; Mission; Morbidity - disease rate; Myelin; Nature; Nerve; Nervous System Trauma; Neurons; Outcome; Outcome Measure; Output; Participant; Pathologic Processes; Pathology; Pathway interactions; Patients; Pattern; Performance; Population; Principal Component Analysis; Probability; Procedures; Process; Questionnaires; Radial; Recording of previous events; Regression Analysis; Reporting; Retrospective Studies; Sampling; Scanning; Sensitivity and Specificity; Services; Severities; Site; Skeleton; Staging; Swelling; System; Training; Training Support; Trauma; Traumatic Brain Injury; Validation; Veterans; War; Weight; Work; base; cognitive testing; cohort; combat; cost; daily functioning; depressive symptoms; design; disability; experience; functional outcomes; health administration; improved; indexing; member; mild traumatic brain injury; neuroimaging; neuropathology; neuropsychological; operation; outcome forecast; post-traumatic stress; public health relevance; rehabilitation service; rehabilitation strategy; statistics; tool; validation studies; white matter; white matter change; white matter injury

Mild traumatic brain injury (mTBI) is the signature injury of the wars in Afghanistan and Iraq. Recent statistics indicate that 60% of blast injuries result in TBI and approximately 20% of returning OEF/OIF Veterans have sustained a TBI, with the majority classified as mTBI. Although many sequelae of mTBI resolve within a few months, a substantial portion of patients experience difficulties for years. Diagnosis of mTBI in the chronic stage is a frequent referral for the Veterans Health Administration. Conventional MRI and CT are typically normal months after civilian and military mTBI making it difficult to accurately diagnose and to determine rehabilitation strategies. Diffusion tensor imaging (DTI) can be used to characterize and quantify WM pathways in the living brain. Specific to brain injury, pathological processes causing loss or disorganization of fibers associated with breakdown of myelin and downstream nerve terminals, neuronal swelling or shrinkage, and increased or decreased extracellular space, could affect the quantitative scalar metrics like mean diffusivity (MD), fractional anisotropy (FA), radial diffusivity (RD), and/or axial diffusivity (AD). Recent studies have reported that FA was reduced in chronic civilian mTBI. Evidence from military cohorts also suggests important changes in DTI metrics across several brain regions. Machine learning (ML) algorithms are particularly sensitive to distributed changes caused by disease as observed in several structural and functional studies. This particular class of algorithms is specifically designed to identify patterns in temporal or spatial data to distinguish between groups. While several ML algorithms exists, one particular multivariate algorithm known as a Support Vector Machine (SVM) has been successfully applied to Alzheimer's Disease studies as well as a recent study in a group of TBI patients through the use of DTI data. In addition, the incorporation of principal component analysis (PCA) to SVM showed robust automated detection of WM degradation in Alzheimer's Disease over several sites and MR scanner platforms. This ability to evaluate this across platforms is particularly attractive to multi-center imaging studies that are performed in the VHA system. At present, the automated detection of biomarkers is scarce in the diagnosis and prognosis of mTBI in our Veteran population. This work will tailor an imaging and detection strategy that can possibly be used to not only identify Veterans with mTBI more objectively but also predict cognitive outcome to help facilitate appropriate rehabilitation strategies. Aim 1 will consist of a retrospective study of 70 subjects and controls to train the SVM algorithm to differentiate between mTBI pathology and uninjured military controls who were also deployed in the OIF/OEF/OND conflicts. DTI skeletons will be processed using Tract-Based Spatial Statistics (TBSS) software and will be used as inputs into the SVM algorithm. Using this data, parameters such as the cost function will be determined to optimize the algorithm. We will measure the accuracy, sensitivity and specificity of the algorithm by using a cross-validation approach. Finally for this first aim, we will use a sensitivity analysis technique to identify regions the algorithm weights more in determining if an mTBI has taken place. This will identify pathways that are vulnerable to injury. In Aim 2, we will use the SVM classifier on DTI scans to output possibility indices of mTBI. Regression analysis will be used to relate these indices to outcome measures. In conclusion, this work will provide a robust tool to not only better diagnose and characterize mTBI but also stratify more personalized rehabilitation strategies through the improved characterization of mTBI.

轻度创伤性脑损伤（MTBI）是阿富汗和伊拉克战争的标志性伤害。最近的 统计数据表明，60％的爆炸损伤导致TBI和约20％的返回OEF/OIF退伍军人 已经持续了TBI，大多数被归类为MTBI。尽管MTBI的许多后遗症在 几个月来，很大一部分患者遇到困难。慢性诊断MTBI 阶段是退伍军人卫生管理局的经常转诊。常规MRI和CT通常是 平民和军事MTBI的正常月份使得难以准确诊断并确定 康复策略。 扩散张量成像（DTI）可用于表征和量化Live中的WM途径 脑。特定于脑损伤，导致纤维损失或混乱的病理过程 髓磷脂和下游神经末端的分解，神经元肿胀或收缩，增加或增加或增加 细胞外空间减少，可能影响定量标量指标，例如平均扩散率（MD），分数 各向异性（FA），径向扩散率（RD）和/或轴向扩散率（AD）。最近的研究报告说FA是 在慢性平民MTBI中减少。军事队列的证据也表明DTI的重要变化 多个大脑区域的指标。 机器学习（ML）算法对疾病引起的分布变化特别敏感 在几个结构和功能研究中观察到。这种特定类的算法是专门的 旨在识别时间或空间数据中的模式以区分组。而几个毫升 存在算法，一种特定的多元算法称为支持向量机（SVM） 成功地应用于阿尔茨海默氏病研究以及一组TBI患者的最新研究 通过使用DTI数据。另外，将主成分分析（PCA）纳入SVM 在多个部位显示出强大的自动检测Alzheimer病中WM降解的自动检测和MR 扫描仪平台。这种跨平台评估这一点的能力对多中心成像特别有吸引力 在VHA系统中进行的研究。目前，生物标志物的自动检测很少 MTBI在我们的退伍军人人口中的诊断和预后。这项工作将量身定制成像和检测 不仅可以用来更客观地识别退伍军人，而且可以预测的策略 认知结果有助于促进适当的康复策略。 AIM 1将包括对70名受试者和对照的回顾性研究，以训练SVM算法为 区分也部署在 OIF/OEF/OND冲突。 DTI骨架将使用基于区域的空间统计（TBSS）软件处理 并将用作SVM算法的输入。使用此数据，诸如成本函数之类的参数将 确定以优化算法。我们将衡量该的准确性，灵敏度和特异性 算法使用交叉验证方法。最后，对于第一个目标，我们将使用灵敏度分析 确定算法权重的技术在确定是否发生了MTBI时。这会 确定容易受伤的途径。在AIM 2中，我们将在DTI扫描上使用SVM分类器进行输出 MTBI的可能性指数。回归分析将用于将这些指数与结果度量相关联。在 结论，这项工作将提供一个强大的工具，不仅可以更好地诊断和表征MTBI，还可以 通过改进的MTBI表征对更个性化的康复策略进行分层。